Statistical analysis as a way of controlling the quality of the product produced by a manufacturing process is well known in the art. Data is collected and various process parameters are measured for conformance with standards. The conformance of the parameters to a range is typically related to a measure of the quality of the product. Variations of the parameters from the accepted range are indicative of a process that is out of control, possibly resulting in products that do not conform to specifications.
One example of a quality assurance process is the six-sigma process. The six-sigma process is a problem solving methodology using statistical tools. The six-sigma process is utilized by many businesses to solve quality and business problems.
Other examples of quality assurance processes or methods include: inspection and testing, validation, risk management, statistical process control, and pre-control.
Quality assurance is especially critical in the manufacture of medical devices, diagnostics and pharmaceutical products. The use of quality assurance processes for the manufacture of medical devices, diagnostics and pharmaceutical in the United States is mandated by law by the U.S. Food and Drug Administration (FDA). The FDA has promulgated Good Manufacturing Practice regulations (GMPs) that must be strictly complied with. The GMPs cover all aspects of the manufacture of regulated products, including controls over design, raw materials, sampling, assembly, testing, storage, sterilization, etc. Given that most FDA-regulated products are mass-produced, it is neither possible nor desirable to quality control test every product prior to release. Some products, such as sterile implantable medical devices or injectable pharmaceuticals, cannot be tested for conformance to specifications after manufacture without destroying or adversely affecting the product. Accordingly, it is important to build quality into a product by the use of various quality assurance processes, as required by the GMPs and other quality assurance methodologies.
Although the GMPs and statistical control systems provide for quality products, there is also a need for e effective predictive systems so that potential risks can be identified and resolved to prevent product that does not conform to specifications from being released, even though the processes conform to GMPs. It can be appreciated that in FDA-regulated industries, it is critical to patient safety and welfare that devices and pharmaceuticals strictly conform to specifications so that they are safe and effective for their intended use. Many medical devices are implanted in patients, and if defective, cannot be readily retrieved and/or replaced without threatening the life or safety of the patients. Failure of critical medical devices may be catastrophic resulting in serious injury to the patient. Similarly, defective pharmaceutical products can be harmful to patients, e.g., non-sterile injectables, dosage strength that is too high or too low, misbranded or contaminated pharmaceuticals, etc. When defects in FDA-regulated products that have been released are discovered, either through reports from the field or otherwise, it is necessary to recall products from distribution to prevent injuries to patients. This entails considerable cost, and is often accompanied by adverse publicity, even though the manufacturer has used its best efforts to manufacture the products in conformance with GMPs. And, as previously mentioned, defective medical devices that have been implanted in patients may not be retrievable.
In the quality systems, processes and methods of the prior art, multiple layers of protection in these systems and methods exist to prevent products containing defects from reaching the customer and consumer. However, when gaps in these systems and methods align (i.e., a perfect storm scenario), a situation may be created wherein a potential risk of product nonconformance is created. None of the previously mentioned statistical quality methods, processes and systems provides the user with a reliable prediction of potential risk in the manufacturing processes utilized to produce products. There is a need for a proactive method of identifying risk in manufacturing processes in order to prioritize and subsequently resource quality improvement efforts in order to provide an additional layer of assurance that product is free from defects. There is a need in this art for novel methods of predicting defects in products based upon an analysis of design, regulatory risk, and the manufacturing process variables associated with these products. Such a method would facilitate a more predictive process of identifying potential risks, thus presenting the opportunity to reduce or eliminate the incidence of manufacturing defects in finished products.